# Project Title
Simplifying Regression and Classification Modeling
## Guide
### Installation setup
`pip install AlgoMaster`
### Classfication model
1. Initialize the model
`Classifier=AlgoMaster.Classifier(X,Y,test_size=0.2,random_state=20)`
2. Train the model and predict the results in table format
`Classifier.model_training()`
3. Ensemble technique
`Classifier.ensemble_prediction(No. of models)`
4. Single Training
To predict unseen data
`data=[1,2,3,4,5,6,7,8,9]
Classifier.logistic_test(data)
Classifier.KNeighbors_test(data)
Classifier.GaussianNB_test(data)
Classifier.Bagging_test(data)
Classifier.ExtraTrees_test(data)
Classifier.RandomForest_test(data)
Classifier.DecisionTree_test(data)
Classifier.AdaBoost_test(data)
Classifier.GradientBoosting_test(data)
Classifier.XGBoost_test(data)
Classifier.SGD_test(data)
Classifier.SVC_test(data)
Classifier.Ridge_test(data)
Classifier.BernoulliNB_test(data)`
5. Hyperparameter Turning
To find the best parameters for the model
`Classifier.hyperparameter_tuning()`
6. Single Hyperparameter Turning
To find the best parameters for the model
`Classifier.logistic_hyperparameter()
Classifier.KNeighbors_hyperparameter()
Classifier.GaussianNB_hyperparameter()
Classifier.Bagging_hyperparameter()
Classifier.ExtraTrees_hyperparameter()
Classifier.RandomForest_hyperparameter()
Classifier.DecisionTree_hyperparameter()
Classifier.AdaBoost_hyperparameter()
Classifier.GradientBoosting_hyperparameter()
Classifier.XGBoost_hyperparameter()
Classifier.SGD_hyperparameter()
Classifier.SVC_hyperparameter()
Classifier.Ridge_hyperparameter()
Classifier.BernoulliNB_hyperparameter()`
### Regression model
1. Initialize the model
`Regressor=AlgoMaster.Regressor(X,Y,test_size=0.2,random_state=20)`
2. Train the model and predict the results in table format
`Regressor.model_training()`
3. Ensemble technique
`Regressor.ensemble_prediction(No. of models)`
4. Single Training
`data=[1,2,3,4,5,6,7,8,9]
Regressor.LinearRegression_test(data)
Regressor.KNeighbors_test(data)
Regressor.Bagging_test(data)
Regressor.ExtraTrees_test(data)
Regressor.RandomForest_test(data)
Regressor.DecisionTree_test(data)
Regressor.AdaBoost_test(data)
Regressor.GradientBoosting_test(data)
Regressor.XGBoost_test(data)
Regressor.TheilSen_test(data)
Regressor.SVR_test(data)
Regressor.Ridge_test(data)
Regressor.RANSAC_test(data)
Regressor.ARD_test(data)
Regressor.BayesianRidge_test(data)
Regressor.HuberRegressor_test(data)
Regressor.Lasso_test(data)
Regressor.ElasticNet_test(data)`
5. Hyperparameter Turning
To find the best parameters for the model
`Regressor.hyperparameter_tuning()`
6. Single Hyperparameter Turning
To find the best parameters for the model
`Regressor.KNeighbors_hyperparameter()
Regressor.Bagging_hyperparameter()
Regressor.ExtraTrees_hyperparameter()
Regressor.RandomForest_hyperparameter()
Regressor.DecisionTree_hyperparameter()
Regressor.AdaBoost_hyperparameter()
Regressor.GradientBoosting_hyperparameter()
Regressor.XGBoost_hyperparameter()
Regressor.TheilSen_hyperparameter()
<!-- Regressor.SVR_hyperparameter() -->
Regressor.Ridge_hyperparameter()
Regressor.RANSAC_hyperparameter()
Regressor.ARD_hyperparameter()
Regressor.BayesianRidge_hyperparameter()
Regressor.Lasso_hyperparameter()
Regressor.ElasticNet_hyperparameter()`
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"description": "\r\n# Project Title\r\n\r\n\r\n\r\nSimplifying Regression and Classification Modeling\r\n\r\n\r\n\r\n## Guide\r\n\r\n\r\n\r\n### Installation setup\r\n\r\n\r\n\r\n`pip install AlgoMaster`\r\n\r\n\r\n\r\n### Classfication model\r\n\r\n\r\n\r\n1. Initialize the model\r\n\r\n\r\n\r\n `Classifier=AlgoMaster.Classifier(X,Y,test_size=0.2,random_state=20)`\r\n\r\n\r\n\r\n2. Train the model and predict the results in table format\r\n\r\n\r\n\r\n `Classifier.model_training()`\r\n\r\n\r\n\r\n3. Ensemble technique\r\n\r\n\r\n\r\n `Classifier.ensemble_prediction(No. of models)`\r\n\r\n\r\n\r\n4. Single Training\r\n\r\n To predict unseen data\r\n\r\n\r\n\r\n `data=[1,2,3,4,5,6,7,8,9]\r\n\r\n Classifier.logistic_test(data)\r\n\r\n Classifier.KNeighbors_test(data)\r\n\r\n Classifier.GaussianNB_test(data)\r\n\r\n Classifier.Bagging_test(data)\r\n\r\n Classifier.ExtraTrees_test(data)\r\n\r\n Classifier.RandomForest_test(data)\r\n\r\n Classifier.DecisionTree_test(data)\r\n\r\n Classifier.AdaBoost_test(data)\r\n\r\n Classifier.GradientBoosting_test(data)\r\n\r\n Classifier.XGBoost_test(data)\r\n\r\n Classifier.SGD_test(data)\r\n\r\n Classifier.SVC_test(data)\r\n\r\n Classifier.Ridge_test(data)\r\n\r\n Classifier.BernoulliNB_test(data)`\r\n\r\n\r\n\r\n5. Hyperparameter Turning\r\n\r\n To find the best parameters for the model\r\n\r\n\r\n\r\n `Classifier.hyperparameter_tuning()`\r\n\r\n\r\n\r\n6. Single Hyperparameter Turning\r\n\r\n To find the best parameters for the model\r\n\r\n\r\n\r\n `Classifier.logistic_hyperparameter()\r\n\r\n Classifier.KNeighbors_hyperparameter()\r\n\r\n Classifier.GaussianNB_hyperparameter()\r\n\r\n Classifier.Bagging_hyperparameter()\r\n\r\n Classifier.ExtraTrees_hyperparameter()\r\n\r\n Classifier.RandomForest_hyperparameter()\r\n\r\n Classifier.DecisionTree_hyperparameter()\r\n\r\n Classifier.AdaBoost_hyperparameter()\r\n\r\n Classifier.GradientBoosting_hyperparameter()\r\n\r\n Classifier.XGBoost_hyperparameter()\r\n\r\n Classifier.SGD_hyperparameter()\r\n\r\n Classifier.SVC_hyperparameter()\r\n\r\n Classifier.Ridge_hyperparameter()\r\n\r\n Classifier.BernoulliNB_hyperparameter()`\r\n\r\n\r\n\r\n### Regression model\r\n\r\n\r\n\r\n1. Initialize the model\r\n\r\n\r\n\r\n `Regressor=AlgoMaster.Regressor(X,Y,test_size=0.2,random_state=20)`\r\n\r\n\r\n\r\n2. Train the model and predict the results in table format\r\n\r\n\r\n\r\n `Regressor.model_training()`\r\n\r\n\r\n\r\n3. Ensemble technique\r\n\r\n\r\n\r\n `Regressor.ensemble_prediction(No. of models)`\r\n\r\n\r\n\r\n4. Single Training\r\n\r\n\r\n\r\n `data=[1,2,3,4,5,6,7,8,9]\r\n\r\n Regressor.LinearRegression_test(data)\r\n\r\n Regressor.KNeighbors_test(data)\r\n\r\n Regressor.Bagging_test(data)\r\n\r\n Regressor.ExtraTrees_test(data)\r\n\r\n Regressor.RandomForest_test(data)\r\n\r\n Regressor.DecisionTree_test(data)\r\n\r\n Regressor.AdaBoost_test(data)\r\n\r\n Regressor.GradientBoosting_test(data)\r\n\r\n Regressor.XGBoost_test(data)\r\n\r\n Regressor.TheilSen_test(data)\r\n\r\n Regressor.SVR_test(data)\r\n\r\n Regressor.Ridge_test(data)\r\n\r\n Regressor.RANSAC_test(data)\r\n\r\n Regressor.ARD_test(data)\r\n\r\n Regressor.BayesianRidge_test(data)\r\n\r\n Regressor.HuberRegressor_test(data)\r\n\r\n Regressor.Lasso_test(data)\r\n\r\n Regressor.ElasticNet_test(data)`\r\n\r\n\r\n\r\n5. Hyperparameter Turning\r\n\r\n To find the best parameters for the model\r\n\r\n\r\n\r\n `Regressor.hyperparameter_tuning()`\r\n\r\n\r\n\r\n6. Single Hyperparameter Turning\r\n\r\n To find the best parameters for the model\r\n\r\n\r\n\r\n `Regressor.KNeighbors_hyperparameter()\r\n\r\n Regressor.Bagging_hyperparameter()\r\n\r\n Regressor.ExtraTrees_hyperparameter()\r\n\r\n Regressor.RandomForest_hyperparameter()\r\n\r\n Regressor.DecisionTree_hyperparameter()\r\n\r\n Regressor.AdaBoost_hyperparameter()\r\n\r\n Regressor.GradientBoosting_hyperparameter()\r\n\r\n Regressor.XGBoost_hyperparameter()\r\n\r\n Regressor.TheilSen_hyperparameter()\r\n\r\n <!-- Regressor.SVR_hyperparameter() -->\r\n\r\n Regressor.Ridge_hyperparameter()\r\n\r\n Regressor.RANSAC_hyperparameter()\r\n\r\n Regressor.ARD_hyperparameter()\r\n\r\n Regressor.BayesianRidge_hyperparameter()\r\n\r\n Regressor.Lasso_hyperparameter()\r\n\r\n Regressor.ElasticNet_hyperparameter()`\r\n\r\n",
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